MSc in Survey Statistics and Data Analytics
Budapest, Hungary
MSc
DURATION
4 semesters
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
31 May 2027*
EARLIEST START DATE
Sep 2027
TUITION FEES
EUR 1,550 / per semester **
STUDY FORMAT
On-Campus
* Application fee changes between suberiods, 31 May is the final deadline
** €1550 per semester for EU students | €3500 per semester for non-EU students
Fast-track counseling
By contacting the school, you'll get access to free priority counselling for any study and application questions.
Key Summary
About: The MSc trains analysts for decisions in business, public administration, social research. You’ll study survey methods, web analytics, network analysis, natural language processing, basic machine learning, and implement analytics in R and Python. Courses cover SQL, Git, data infrastructure, math, visualization, project management, communication. The programme includes a six-week internship.
Career Outcomes: Graduates work in public administration, finance, market and opinion research, marketing, healthcare, pharmaceuticals, non-profit, info-communication, and academia. Typical roles include Data Scientist, Business Intelligence Analyst, Survey Statistician, Healthcare Analyst, Data Analyst. The degree prepares you for doctoral studies in applied mathematics, statistics, social sciences, and computer science.
The data revolution is creating a need for data analytics in a wide variety of fields. We have developed our programme in response to this need. Our goal is to train data analysts who can contribute to data-driven decision-making in business/industry, public administration, or social research.
Our students will be able to analyse large databases, be familiar with statistical solutions for survey-based research, and conduct online research and web analytics in scientific, governmental, and business applications. They will have knowledge of network analysis, and natural language processing, be familiar with the basics of machine learning, be prepared to implement analytic solutions in R and Python and know the basics of data analysis infrastructure (SQL, Git, and other tools). We will also provide the mathematical foundation that is essential for a deep understanding of methods and for lifelong learning.
The program also provides students with practical knowledge that introduces them to the entire process of data analytics (project management, data collection, data visualization, and business communication). The programme is recommended for applicants seeking a complex programme that is flexible while providing a solid foundation in data analytics.
Strength of the program
The program has been running successfully in Hungarian for 30 years and is one of the most popular master's programmes at ELTE. Its founder is Tamás Rudas, Professor at the Department of Statistics at the University of Washington, former Director General of the Center for Social Science Research of the Hungarian Academy of Sciences, Fellow of the European Academy of Sociology, and former President of the European Association of Methodology.
The success of the programme is based on continuous improvement and adaptation to changing social contexts. The ideal data analyst has three main skills: statistical, programming, and business/social research skills. Accordingly, we provide strong mathematical/statistical/machine learning skills to extract new insights from the data. Programming skills are also provided for effective data management. Business/social research knowledge is provided to understand the context of the data.
In addition, we try to develop the creativity needed to ask good questions, select good data, and find good interpretations. Our strength is that, beyond the technical side of analytics, we also aim to provide the business context so that our students can independently translate research questions into analytical problems and then translate the results back to the client.
Tracks/Specializations:
There are no specializations, but students can tailor their learning path to their own interests as the electives are organized into the following modules of two to three courses: Biomedical Research, Economic Research, Digital Data Analytics, Social Research, and Business Research.
The program is divided into 5 mandatory blocks:
- Mathematical foundation (mathematical foundation, linear algebra, probability theory, mathematical statistics)
- Data collection (data collection methods and sampling, survey data processing)
- Basics of business research (market research, communication and project management, project practice)
- Programming (R, Python, Github, SQL)
- Data analysis (multivariate probability theory and statistics, data science, data analysis)
- Applications (qualitative research, social studies, network analysis)
Elective professional modules
In addition to the mandatory modules, we also offer differentiated professional modules with elective courses:
- Biomedical Research
- Economic Research
- Digital Data Analytics
- Social Research
- Business Research
The program has the advantage of adapting to the complex needs of the labour market. Our graduates can design research based on either questionnaires or digital or administrative data sources. We want to provide our students with the opportunity to develop a learning path and career path that suits their own interests, whether they want to use applications of data analytics based on artificial intelligence or quantitative tools of market research.
Our program includes a six-week internship, and we have decades of experience working with renowned business figures. This, combined with the knowledge gained in the program, enables our graduates to choose from a wide range of job opportunities. They are typically employed in data-intensive sectors: public administration, info-communication, financial services, market and public opinion research, marketing, non-profit sector, healthcare and pharmaceuticals. At the same time, many of our graduates are working in research institutions or in higher education, as the program also prepares them for doctoral studies, potentially in all the disciplines covered by the program (applied mathematics, statistics, social sciences, computer science).
Job examples
- Data Scientist
- Business Intelligence Analyst
- Survey Statistician
- Healthcare Analyst
- Data Analyst